Predictive Analysis of Chikungunya
This paper addresses the problem of forecasting Chikungunya incidence rates, which is an incremental contribution to public health surveillance.
This paper analyzed a DARPA dataset of Chikungunya incidence rates from 2014-2017, augmenting it with weather features (temperature, humidity, dewiness, wind, pressure) and geographical coordinates. Using Linear Regression, they predicted incidence rates and calculated accuracy and error rates.
Chikungunya is an emerging threat for health security all over the world which is spreading very fast. Researches for proper forecasting of the incidence rate of chikungunya has been going on in many places in which DARPA has done a very extensive summarized result from 2014 to 2017 with the data of suspected cases, confirmed cases, deaths, population and incidence rate in different countries. In this project, we have analysed the dataset from DARPA and extended it to predict the incidence rate using different features of weather like temperature, humidity, dewiness, wind and pressure along with the latitude and longitude of every country. We had to use different APIs to find out these extra features from 2014-2016. After creating a pure dataset, we have used Linear Regression to predict the incidence rate and calculated the accuracy and error rate.